1. Introduction: The Need for Intelligent LLM Routing
Enterprises increasingly rely on multiple AI models to meet diverse requirements—ranging from coding assistance and data analysis to creative content generation. However, managing these models individually can become cumbersome, expensive, and prone to downtime. Requesty provides a unified solution through “intelligent LLM routing,” acting as a central controller that automatically directs each AI request to the optimal model based on task complexity, cost, and availability. This approach allows organizations to:
Avoid single-provider dependencies that can result in outages or rate-limits
Maintain high reliability and performance
Optimize spend across various model tiers
2. Core Capabilities of Requesty
2.1 Always-Online Reliability
Challenge: AI outages or slowdowns can stall mission-critical workflows and impact user satisfaction.
How Requesty Solves It:
Multi-Provider Redundancy: Requesty monitors the health and uptime of multiple LLM providers (OpenAI, Anthropic, Deepseek, etc.). It automatically fails over to alternative models if one service experiences downtime or degraded performance.
Fallback Policies: Enterprises configure fallback chains (e.g., “Primary → Secondary → Tertiary”) so that if the primary model is unavailable, the router immediately retries the request on the next best option.
Load Balancing & Automatic Retries: Requesty’s routing engine balances traffic across different models and attempts retries on errors, ensuring seamless end-user experience despite transient failures.
https://www.requesty.ai/solution/routing-optimizations
2.2 Cost Efficiency & Spend Optimization
Challenge: Large-scale LLM usage can be prohibitively expensive if every query goes to a premium model.
How Requesty Solves It:
Intelligent Model Selection: Requesty identifies task complexity and routes simple requests to more cost-effective models—reserving the top-tier, higher-cost models for critical or complex tasks.
Usage Analytics & Alerts: Built-in dashboards display real-time spend, token usage, and per-model costs. Enterprises can set budget thresholds and trigger automatic routing adjustments when nearing spend limits.
Custom Policies: Companies easily define business rules (e.g., “Use Model X if total monthly spend under $5k; otherwise switch to Model Y”) to keep costs predictable.
2.3 Smart Model Selection
Challenge: No single LLM is universally “best.” Some excel at code generation, others at creative writing or factual Q&A.
How Requesty Solves It:
Automated Classification: Requesty classifies incoming prompts (e.g., “coding,” “analysis,” “creative text”) and dispatches them to the model optimized for that category.
Model Catalog & Observability: Requesty offers a robust catalog of 150+ models with their capabilities, token limits, and latency stats. Organizations can see exactly which models are being used, how often, and with what results.
Task-Level Optimization: If a query is identified as code-related, Requesty may pick an Anthropic Claude variant tuned for coding. For general-purpose Q&A, it might route to OpenAI GPT-4o. The system learns over time which assignments yield the best performance.
https://www.requesty.ai/solution/smart-routing
3. How Requesty Works in Practice
Incoming Request: A user or application makes an AI call (e.g., asking for code, generating marketing copy, analyzing data).
Routing Decision: Requesty classifies the request by type, complexity, and organizational policies, then selects the best-fit model.
Failover & Fallback: If the selected model is unavailable or errors out, Requesty instantly tries another provider.
Response & Logging: The chosen model returns a response, and Requesty logs the request path (model used, tokens consumed, latency) for analytics and reporting.
This entire flow typically occurs in milliseconds—ensuring a seamless user experience.
4. Benefits to Enterprise AI Teams & CTOs
Guaranteed Uptime: Automatic failover across providers ensures AI-driven processes are never disrupted by a single model outage.
Cost Visibility & Control: Granular spend analytics help AI leaders manage budgets, set policies, and avoid surprise costs.
Flexibility & Scalability: Easily add or remove models to adapt to new use cases, performance requirements, or vendor pricing changes.
Simplified Maintenance: Unified routing means fewer individual integrations. Teams can manage multiple models through a single Requesty API.
Future-Proofed AI Stack: As new, more powerful or cost-efficient LLMs emerge, they can be swapped in with minimal disruption.
5. Key Observability & Monitoring Features
System Uptime Dashboards: Track real-time provider performance and 30-day uptime.
Cost Analytics: Monitor per-model spend and forecast monthly usage trends.
Performance Metrics: View latency, success rate, and concurrency across models to identify bottlenecks.
Custom Alerts & Thresholds: Receive notifications if a model’s error rate spikes or if budget usage nears a set limit.
6. Implementation Best Practices
Start Simple: Configure basic fallback chains for critical workloads (e.g., GPT-4o → Claude → Deepseek).
Leverage Smart Routing: Enable the built-in classification engine to automatically detect request types and assign them to specialized models.
Continuous Tuning: Use Requesty’s analytics to refine routing rules, watch for cost anomalies, and identify opportunities for further optimization.
Ensure Security & Compliance: Store model API keys securely; if you handle sensitive data, route it to an on-prem or compliance-certified model.
7. Conclusion
By intelligently distributing AI requests among multiple large language models, Requesty delivers an always-online AI service with robust failover and real-time cost optimization. Its unified routing approach ensures minimal downtime, predictable spend, and task-tailored model selection—all critical elements for enterprise-grade AI deployments. For CTOs and AI teams seeking a scalable, future-proof way to leverage multiple LLMs, Requesty stands out as a comprehensive platform that harmonizes performance, reliability, and economic efficiency.